Create convolutional model/neural network layers diagram
generic-github-user opened this issue · comments
For reference, the model currently looks like this:
Here's the (slightly abridged) model creation code:
const model = tf.sequential();
model.add(tf.layers.conv2d(
{
inputShape: [res[0], res[1], 1],
filters: conv_filters,
kernelSize: 3,
strides: 1,
activation: 'relu'
}
));
model.add(tf.layers.maxPooling2d(
{
poolSize: 2,
strides: 2
}
));
model.add(tf.layers.conv2d(
{
filters: conv_filters,
kernelSize: 3,
stride: 1,
activation: 'relu'
}
));
model.add(tf.layers.maxPooling2d(
{
poolSize: 2,
strides: 2
}
));
model.add(tf.layers.conv2d(
{
filters: conv_filters,
kernelSize: 3,
stride: 1,
activation: 'relu'
}
));
model.add(tf.layers.maxPooling2d(
{
poolSize: 2,
strides: 2
}
));
model.add(tf.layers.flatten({}));
model.add(tf.layers.dense({units: 32}));
model.add(tf.layers.leakyReLU());
model.add(tf.layers.dropout(0.8, {rate: 0.8}));
model.add(tf.layers.dense({units: 1}));